1. Field of the Invention
The present invention is directed to automated service management, especially in connection with quick-service restaurants.
2. Description of Related Art
The quick-service restaurant (QSR) industry emphasizes rapid customer service in their service queues. Queue length and total wait time impact consumer satisfaction in several ways:
Unacceptably Long Wait Times: Repeated “waited too long” experiences create a negative bias against that brand, resulting in a potential loss of repeat business;
Walk-offs or Drive-offs: If the customer feels that he or she is waiting “too long”, they may leave the queue, resulting in an immediate loss of sales;
Walk-bys or Drive-bys: If a customer approaches an establishment where the queue is perceived to be “too long” then the customer may decide against choosing that brand, resulting in a lost sales opportunity; and
Inattentive Service Personnel: A customer may reach the head of the queue and then be forced to wait because there are no retail employees available to provide service. Lack of attention to the customer is felt to create a more heightened negative perception of the retail establishment's service quality, resulting in a potential loss of repeat business.
Human management is the state-of-the-art in the QSR industry. Human managers are responsible for overseeing the staffing, labor allocation, attentiveness, and service quality of their service personnel.
The effectiveness of human managers is highly constrained by the volume of restaurant traffic. As traffic volumes increase, the ability of a human manager to simultaneously measure service needs and monitor service personnel greatly diminishes. In particular, humans suffer from an inability to simultaneously track the state of several targets—i.e., customers who need service or employees who provide those services. Thus the traffic volumes normally seen in retail establishments, with dozens of customers and employees, are often too high to enable service measurement tasks to be performed reliably by hand.
Human managers are supported by a number of technology solutions that provide measurements of service times; however, there are no known solutions for automatically monitoring service personnel, nor are there general automated service management systems. Most service time measurement systems focus on measuring drive-through times in a manner discussed hereinafter.
Retail industries note that their ability to provide timely service is often put at risk by lack of employee attention to customer needs. Customer needs vary considerably, but include:
Distracted Service Personnel—A person entering a restaurant may in fact be the only person in line, but be forced to wait for 90 seconds before a front counter service person, engrossed in an administrative task, notes that there is a customer requiring attention. In another example, a person may pull into a restaurant property to pick-up a take-out order, enter the restaurant and wait several minutes for someone to notice them at the front-counter register; and
Absent Service Personnel—A person may approach a restaurant ordering station to find that there are no cashiers at their stations—then wait several minutes until a cashier returns. In another example, a person may be waiting for a restaurant to cook their hamburger, only to discover that the cook left the kitchen area and is unaware that there is an order.
In each of these examples, the industry suffers from an inability to ensure that service providers (cooks, cashiers, etc) are both in-position and aware of a service need (customer order, pick-up customer, etc.) in a timely fashion.
The industry's ability to deliver high quality service to their customers is limited by the capacity of service managers to oversee their service management staff in real-time—i.e., at the moment that problems are actually occurring and can therefore be quickly resolved. A service manager's attention is split across many duties. At any moment in time, the service manager may be calling employees to find replacement staff for the day; filing administrative reports like workers compensation claim responses; ordering additional product; interviewing new hires; etc. Each of these tasks competes with and takes away from the amount of attention that the service manager can focus on service.
There are many potential events that might warrant notification to the service manager or crew, including:
Excessive Wait Time—Alert the manager in the event that a customer's wait time has grown excessive. This alarm might include information such as the particular queue that contains the customer; their position in line (i.e., head of the queue, end of the queue, etc.); the actual elapsed time in line; etc.;
Growing Wait Time—Alert the manager in the event that successive customer's wait times are growing—i.e., the (n+1)th customer's wait time is larger than the nth customer's;
Stopped Queue—Alert the manager if a queue fails to move forward after a certain time. For example, in a drive-through, this might indicate the need to ask the first vehicle in the queue to pull to the side because they are waiting for a food product to be completed;
Growing Queue—Alert the manager if the queue is growing. This alert might indicate that the queue has either grown beyond a certain length (e.g. 5 vehicles in the drive-through) or is growing faster than a certain rate (e.g. the addition of more than three vehicles within a 1 minute period);
Unattended Station—Alert the manager if a service employee has left their station unattended. Alternatively, provide an alert only if a customer arrives at the unattended station; and
Excessively Unattended Station—Monitor each station to determine if it is habitually unattended, perhaps modulated by the requirement that it is unattended while a customer is waiting.
The following applications have been identified as requiring human management in the QSR environment:
1. Drive-Through Service—In this application, vehicles queue up at a drive-through.
2. Counter-Service—In this application, patrons queue up at a counter service area.
3. Pull-up or Take-Out Service—In this application, vehicles drive into designated parking spaces in order to wait for delayed food or to pick-up take-out food.
Drive-Through Service:
In drive-through service environments, service managers operate volume businesses, where customers tend to seek service at specific times of the day. For example, restaurants see heavy traffic volumes at meal-times. The drive-through service manager seeks to maximize their throughput during heavy-volume periods in order to capture as many sales as possible.
Generally, a drive-through service manager's service quality metrics focus on helping them to capture as many sales opportunities as possible. Some of these metrics include:
Average Service Time (AST)—By minimizing the average amount of time that each service transaction consumes, more customers can be served during high-volume periods. AST management focuses on creating service opportunities in a limited period of time. This metric is frequently most important to drive-through managers because it drives top-line revenue opportunity;
Total Service Time—Drive-through service managers attempt to minimize the total amount of time that consumers spend waiting for service. The QSR industry believes that excessively long service times frustrate customers, damage the brand, and cost them repeat business; and
Queue Length—The drive-through queue length is the principal information available to potential drive-through consumers to decide whether they will purchase from a retail establishment—if the queue is too long, consumers will drive-by in search of a competitor with a shorter anticipated wait time. Long queue lines cause retail businesses to lose sales opportunities by scaring off customers.
The drive-through service management problem is therefore to manage both the customer's perception of their anticipated wait time (i.e., queue length) and the actual customer wait time to minimize lost sales opportunities (drive-bys and disgruntled customers) while maximizing today's throughput (top-line revenues).
Service managers can improve their service performance in two ways—first, managing the allocation of labor resources assigned to the service queue and second, managing the efficiency of those labor resources.
To this end service managers seek to schedule the fewest number of employees possible (i.e., control their labor costs) while meeting their service targets (i.e., speed-of-service, queue length, etc.). This labor allocation task requires managers to determine the crew size that will be on-hand each hour of the working day and therefore defines the maximum labor with which they can achieve their goals;
Moreover, service managers assign individual service tasks (i.e., cashier, runner, teller, etc.) to the members of that days work crew. Individual industries vary in the flexibility with which crew members can be reassigned tasks. Crew members may also require specific skills, training, or certification in order to perform certain tasks. This second labor allocation task requires managers to map crew members onto labor assignments in a way that they feel will maximize the efficiency of the total crew;
Lastly, service managers oversight employee performance in real-time so that they can coach, prod, push, encourage, identify and remove hurdles, respond to unusual events, and otherwise increase the overall crew's ability to execute against their tasks;
The state-of-the-art in drive-through service measurement is the drive-through timer, commonly employed in the quick-service restaurant industry. The drive-through timer times automotive traffic between two points—usually the order board and the pick-up window, although some systems also time from a third point, often in advance of the order board.
Drive-through timers operate on one of two principals. The first type is operated by employees through the point-of-sale (i.e., electronic cash register) system and time traffic between the order board and the pick-up window. A timer is started when the order-taker (an employee) presses the first button to enter a new drive-through order into the POS. The timer is stopped when the order-server (another employee) presses a “served” button on an apparatus mounted near the pick-up window. The second employee is trained to press the “served” button when they deliver the food through the pick-up window.
The second type automatically starts and stops timers when the vehicle drives over an electromagnetic loop sensor buried in the drive-through pavement. Such systems may be stand-alone, or may operate in conjunction with (i.e., provide input to) the point-of-sale systems described above—e.g. replace the first button push with a loop sensor to start the timer.
Drive-through timers of the first type (i.e., employee-driven systems) are vulnerable to employee cheating. Employees who are driven to achieve an average service time target can improve their times by pushing the “serve” button before they have actually served the food to the customer. Thus, the recorded time no longer actually corresponds to the physical events that the drive-through timer purports to measure—i.e., the time that elapses between the start of the customer order and the moment that the customer receives food.
Drive-through timers of the second type (i.e., loops in the ground) are limited in their measurement flexibility because their measurement points are fixed. Once loops are buried in the ground, the system can only measure elapsed time between those two points. This is an important limitation as it does not allow the system to record any portion of the customer's wait that precedes the first loop. Moreover, any vehicle that enters the systems in a way that bypasses the first loop cannot be properly timed. Finally, any vehicle that exits the system in a way that bypasses the second loop cannot be properly timed.
Error recover is another limitation of drive-through timers of the second type. Specifically, such drive-through timers are prone to timing errors when vehicles fail to cross or trip both timing sensors. Because these systems assume vehicle continuity between the sensors, any vehicle that enters or leaves the line between the sensors causes a mismatch in the start and end times for the other vehicles in the queue. Since there is no way known to detect this error condition, incorrect timings will continue until the queue clears out and the system can be reset.
Drive-through timers are further limited by their inability to identify important exceptional events, like drive-offs. Drive-through service managers would benefit greatly from a system that could determine when drive-offs (i.e., vehicles that begin to move through the drive-through, but drive away before reaching the service point) occur and how long drivers waited before driving off. Because drive-through timers only measure vehicles at two specific points, the vehicles are basically invisible to the systems at all points in-between. Moreover, there is no way to disambiguate between a vehicle passing over a sensing point because it is traveling in the drive-through lane versus a vehicle that is passing over the sensing point because it is driving off.
Finally, drive-through timers have an inherent lack of flexibility in the events that may be employed to trigger their timers. Drive-through timers of the second type are started and stopped based strictly on the presence of an un-differentiable vehicle over the sensor position. Once installed, the drive-through manager cannot easily change the point at which the timers start and stop.
Drive-through timers of both types are limited in the event class that can be used to trigger the timers. Drive-through timers of the first type are limited to start the timer upon a particular ordering event (i.e., the first button push of the POS) or upon a particular position event (i.e., the presence of a vehicle over the sensor) or the Boolean combination of the two. Drive-through timers of the first type are theoretically less limited in the event class that can be used to start or stop the timers as humans can be trained to press buttons upon recognizing more complex events. While recognizing that drive-through timers of the first type could theoretically overcome flexibility limitations, pragmatically, humans are limited in their consistency—boredom and distraction inherently limit human performance. Further, drive-through timers of the second type are inherently limited to starting and stopping timers based solely on positional information.
Neither drive-through timer is capable of expanding to measurements of more complex actions or driving behaviors such as “timing from the first time that the vehicle stops while in the lane until the first time that the vehicle stops in front of the order board”.
Moreover, neither drive-through timer is capable of providing constant knowledge of the queue state so that drive-through managers can react in real-time to:
Prioritize drive-through service—Managers could reduce the length of the queue or react to growing wait times by prioritizing the fulfillment of drive-through service over other service queues. Note that this management action is simple prioritization with no change to labor allocation;
Allocate or re-allocate labor—Managers could change labor allocation at the drive-through, either through improved scheduling (i.e., scheduling based on more quantitative information about drive-through performance trends) or by temporarily re-allocating labor in real-time in reaction to poor drive-through performance (i.e., wait times are growing, so reallocate more service personnel to attend to drive-through tasks);
Know when prodding (of service personnel) will help—Management prodding loses its effect when it happens too often and/or without justification. Accurate measurements of the queue state enable managers to know when to prod and to be able to show crew members why it's important to do so now. A running clock or a flashing light indicating too many vehicles in the drive-through add urgency to the manager's orders for faster service; and/or
Identify and react to exceptions—Managers are constantly distracted. A system that could provide constant knowledge of the queue state could enable managers to react to service deficiencies in real-time to, therefore, minimize service disruptions.
If such queue state information were available, managers could also make use of such information to improve their labor scheduling practices. They might, for example, review wait times versus labor statistics throughout the day; look for patterns in unacceptable wait time performance; and review labor allocation in light of this new information.
Moreover, if such queue state information were available, managers could make further use of such information to develop better “queue service models”—i.e., understand the performance limitations of current service practices and make process changes. Queue state analyses could be performed in real-time to identify “bottlenecks” or “frustration points” in the queue model, thereby allowing managers to concentrate their management time on resolving specific service issues.
This last point is especially important to cost-effective training. The ability to understand the specific points in the queue service model that limit service speed or quality means that corporate training funds can be narrowly focused on resolving specific problems, rather than being spent on general service training.
Lastly, if such queue state information were available, managers could use drive-off information derived from such information to improve their target performance times. Drive-offs provide the managers with some information on the limitations of their customer's patience. By maintaining a database of drive-off events, managers could learn about the willingness of their local clients to wait in queues of given queue length, elapsed wait time, and other factors.
Counter Service:
Quick-service restaurants require people to queue up in order to obtain access to a product or service. Counter service, in this sense, is meant to indicate a single queue or two or more parallel queues, as are normally seen in QSRs.
Counter service managers have the same basic business objectives as drive-through service managers: to maximize throughput (i.e., sales revenues during busy periods) at the lowest possible labor costs, while maintaining acceptable customer service levels.
Counter service managers might be less concerned than drive through managers about the impact of queue length on a shopper's decision to frequent their store, as many shoppers are felt to have made a higher “commitment” once they've come into the store. The sunk cost of having already parked and entered the store, combined with the anticipated future cost of getting into another busy queue at another busy store, discourages customers from switching.
Counter service managers can intercede to improve counter service in several ways:
Open or close a register—Counter service areas, unlike the drive-through, frequently have more than one service register. Managers can decide, in real-time, to temporarily open a second register to relieve a growing queue;
Labor Scheduling and Labor Assignment—Front counter managers schedule their counter labor in anticipation of weekly demand projections. This non-real-time approach defines the front counter labor that will be available to meet demand. Counter managers can also re-task their labor force in real-time by shifting crew members from one job assignment to another to best meet real-time changes in demand;
Prodding—Service managers oversight employee performance in real-time so that they can coach, prod, push, encourage, identify and remove hurdles, respond to unusual events, and otherwise increase the overall crew's ability to execute against their tasks;
Problem Resolution—Counter service managers identify and resolve problems that hold back the flow of the queue. Problems like price checks; consumer questions; or employee difficulties with point-of-sale (POS) systems can slow the service lines unnecessarily.
Unlike drive-through service, where timers are a readily available technology, albeit problematic, there is/are no known means to address counter service measurement needs. The principal difference between drive-through and counter service is: drive-throughs are occupied by large, heavy, slow moving metallic objects (e.g., cars, trucks, and other like vehicles) that maintain a relatively simple straight line; counter service areas are occupied by several people in far more complex states of motion (especially children) and often with no real queue structure.
For optimal operation of a QSR, counter service management requires constant knowledge of the queue state so that managers can react in real-time to:
Open or close a register—Managers can open additional counter service registers to accommodate growing traffic. If predictive information were available, managers could open registers in advance of actual demand changes—further helping to reduce consumer wait times;
Prioritize counter service—Managers could react to growing wait times by prioritizing the fulfillment of counter service over other service queues. Note that this management action is simple prioritization with no change to labor allocation—i.e., no one changes job positions;
Allocate or re-allocate labor—Managers could change labor allocation at the counter, either through improved scheduling (i.e., scheduling based on more quantitative information about counter performance trends) or by temporarily re-allocating labor in real-time in reaction to poor counter performance (i.e., wait times are growing, so reallocate more service personnel to attend to counter tasks);
Know when prodding will help—Management prodding loses its effect when it happens too often and/or without justification. Accurate measurements of the queue state enable managers to know when to prod and to be able to show crewmembers why it's important to do so now; and/or
Identify and react to exceptions in real-time to minimize the service disruption.
Take-Out/Pull-Ahead Service:
Both QSR and fast-casual restaurants often designate specific parking spaces or pull-ahead areas for customers who are waiting to either pick-up a previously placed take-out order, or have been asked to pull ahead from a drive-through pick up window because their food is not yet ready. In the case of take-out parking spaces, employees watch for customers to arrive and bring products out. In the case of pull-ahead spaces, employees run food out to the customer as soon as it is ready.
Fast-casual restaurants compete with quick-service restaurants by offering curbside or take-out service as a means of providing the same “fast” service with better quality food. Designated parking spaces are a means of providing additional convenience to the consumer to attract additional sales opportunities from those patrons, such as busy mothers or senior citizens, who don't want to get out of their vehicles.
Quick-service restaurants use pull-ahead spaces to ensure that the drive-through lane does not stop because food is not ready for the patron at the drive-through pick-up window. The pull-ahead space may simply be an unmarked space along the drive-through curb; or it may be a space designated by a box, a large circle, or some other demarcation; or it may be an actual parking space designated for drive-through pull-ahead customers.
Pull-aheads are caused by production anomalies—production was not able to deliver the customer's food in enough time to ensure that that customer's wait didn't begin to impact other customers in the drive-through queue.
Take-out service requires managers or their designees, who are likely to be distracted by other tasks, to keep watch on designated parking spaces and send an employee out with the consumer's food. The manager must make a decision about how much scarce labor to allocate to watching for arriving customers, versus performing other service tasks.
Drive-through service managers must determine (i) when to ask drive-through patrons to move their vehicles into pull-ahead spaces; and (ii) when to send employees out to deliver the patron's food.
The state of the art in take-out or pull-ahead service management is manual management—i.e., people assigned to the task of monitoring parking spaces (either directly or through a camera system) and sending employees to deliver product. There are no known technology solutions to this management problem.
It would, therefore, be desirable to overcome the above problems by providing a system and a method for real-time electronic determination of: the effectiveness of customer service; the prediction of service demand needs; and the identity of real-time service problems. Still other problems the system and method overcome will become apparent to those of ordinary skill in the art upon reading and understanding the following Detailed Description.